SMWW: Analytical Methods in Laboratory Quality Control

Learn about SMWW, an analytical method reference used in laboratory quality control. 

When we think of a testing laboratory, it’s natural to picture accurate results and high reliability. But making sure those assumptions actually hold true isn’t nearly as simple.

That’s why, in this article, you’ll understand where to start your efforts to get your lab’s Quality Assurance and Quality Control stages right the first time, with the help of analytical methods — using SMWW as an example.

Quality Assurance and Quality Control in the laboratory

Having reliable results is a requirement for any laboratory. And for that, every measurable variable possible must be kept under observation throughout the entire process. To guarantee this, the monitoring stages known as Quality Assurance and Quality Control are indispensable. It may sound redundant to talk about assurance and control, but they are not the same thing.

Quality Assurance ensures that quality requirements, policies, and procedures are being met, functioning in a way similar to an audit.

Quality Control, on the other hand, focuses specifically on the processes, verifying that every step, from sample intake to disposal, complies with a pre-established standard.

And when it comes to standardization in the laboratory, it’s essential to talk about analytical methods. To understand how to apply them, let’s use SMWW as an example.

SMWW

Several references can be consulted for implementing quality control in a laboratory, but in this article we’ll take a closer look at a widely used standardized reference that describes analytical methods for samples in an aqueous matrix: Standard Methods for the Examination of Water and Wastewater (SMWW).

SMWW is a very rich source, not only of validated analytical methods, but also of quality control parameters, both for the laboratory as a whole and for specific methods.

The book presents test descriptions with specifications and, when possible, alternative ways of carrying out certain steps. It is therefore not a book of ready-made scripts, but rather a guide for laboratories to develop and standardize their analyses in whatever way best suits their reality.

To make good use of the methods and get the most out of the knowledge contained in SMWW, it’s necessary to understand how it’s organized.

The book is divided into parts, each part covering a type of analyte and containing all the validated and recommended methods for it. The first part provides an introduction and an explanation of the precautions a laboratory should take.

It’s in this first part that an initial approach to Quality Assurance and Quality Control is presented, in chapter XX20, which covers their necessity and importance, as well as a definition of the terms and types of controls commonly used.

This chapter provides general knowledge on the subject and is essential reading for understanding it. To access this chapter, just click here.

When choosing an analysis method, the analyst will find, in its respective chapter, guidance on how to perform it along with certain necessary quality control steps.

But beyond that, in the section covering the analyte of interest, there is a chapter XX20 that contains other very valuable information on the subject, such as explanations of how each type of control should be carried out and, most importantly, tables indicating the recommended types of control for each specific test.

It’s worth noting that the laboratory has the authority to follow the recommendations to the letter, or to add or remove steps, based on observations and evaluations of the results obtained.

Therefore, the Standard Methods for the Examination of Water and Wastewater, containing such rich information on quality control, are essential to help implement a quality control and assurance system in a laboratory.

By combining the fundamentals presented in the book’s introduction, at the start of each part, and within the method itself, it’s possible to carry out analyses as accurately as possible, minimizing errors and increasing the reliability of the results obtained.